“data pipeline”的搜索结果

创建和编辑Excel、CSV等表格文件，整理混乱数据并转换格式，添加公式、图表和格式设置，重新计算公式并检查错误，交付可随输入变化更新的电子表格。

• 将混乱的CSV整理成表格
• 为Excel添加公式和图表
• 创建供填写的模板

Create, edit, and clean spreadsheet files, including Excel workbooks and CSV data. Add formulas, formatting, and charts, preserve existing workbook conventions, and recalculate formulas with error checks before delivering the spreadsheet.

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• Clean messy CSV data into a spreadsheet
• Edit an Excel workbook while preserving its formatting
• Create a financial model with recalculating formulas

Improve slow Apache Spark jobs by reviewing data partitions, caching, memory settings, and shuffles, which move data between workers. Use the guidance to investigate bottlenecks and tune pipelines for larger datasets.

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• Investigating slow Spark jobs
• Tuning memory and executor settings
• Reducing data skew and shuffle traffic

Prepare raw credit data for pre-loan modeling by checking data quality and screening variables. Review missing values, predictive usefulness, stability, noise, and correlation, then export an Excel report with organization-level statistics and cleaning results.

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• Prepare credit data before building a model
• Compare missing values and bad-sample rates across organizations
• Screen variables for usefulness, stability, and redundancy

Check training data and machine learning models for malicious changes, including backdoors that cause unexpected behavior on specific inputs. Combine data quality checks, model analysis, and file integrity verification, then document findings and retest after remediation.

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• Screening third-party data before training
• Checking downloaded model weights before deployment
• Investigating unexpected behavior on specific inputs

Review sales opportunities by stage to see deal totals, aging, and risks. Identify stalled deals, overdue closing dates, and missing next steps, then compare available opportunities with sales targets and recommend where to focus.

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• Preparing a weekly sales pipeline review
• Finding stalled deals and overdue closing dates
• Checking whether opportunities cover a sales target

Run nf-core workflows on local sequencing files or public GEO/SRA datasets to analyze gene expression, genetic variants, or chromatin accessibility. The skill guides environment checks, sample table preparation, workflow configuration, execution, and output verification.

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• Analyze gene expression from RNA-seq data
• Identify genetic variants in WGS/WES data
• Analyze chromatin accessibility from ATAC-seq data

Design MongoDB data structures around how data is accessed, write queries, and build staged data transformations for aggregation. Review indexes and query execution statistics to identify performance issues and choose suitable approaches for related or time-series data.

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• Choosing between embedded and referenced data
• Writing search queries and aggregation pipelines
• Reviewing slow queries and index usage

Guides adding metadata, extra information attached to data elements, to Apache Beam's WindowedValue. Maps the changes needed across serialization, storage, processing, and runner interfaces so new fields remain available during pipeline execution.

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• Adding trace context or drain flags to WindowedValue
• Checking metadata preservation through Windmill storage
• Reviewing propagation through grouping and timers

Guides the design of data pipelines, continuous stream processing, and data quality checks. It compares tools such as Airflow, Dagster, and Flink, and provides implementation patterns and checklists for scheduling, retries, validation, and monitoring.

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• Compare tools for data pipeline scheduling
• Design continuous data processing
• Define checks for missing, duplicate, or stale data

Collect papers across research databases, turn research questions into targeted search queries, remove duplicate records, and extract available PDF links. Useful for preparing systematic literature reviews, with database selection and access checks before retrieval.

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• Collecting papers across multiple research databases
• Turning research questions into database search queries
• Removing duplicate records and finding PDF links

Design and implement repeatable workflows that clean, encode, transform, and validate raw data for machine learning. Get a preprocessing plan or implementation outline, with clear steps and guidance on where further checks are needed.

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• Preparing raw data for machine learning
• Automating recurring data cleaning
• Planning data extraction, transformation, and loading

Data engineering for batch and streaming pipelines with Airflow, dbt, Spark, and Kafka. Use when designing data architectures, building pipelines, adding data-quality checks, optimizing ETL/ELT, or troubleshooting pipeline failures.

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Designs and builds ETL/ELT data pipelines. Takes data sources, destination, transformation requirements. Generates pipeline code (Python/SQL), scheduling config, error handling, monitoring setup, and data quality checks. Outputs data-pipeline-spec.md + implementation files.

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Canonical entry point for the full Gaia evidence verification pipeline. Invoke as /evidence-verification-pipeline or /ev-pipeline — both names trigger this skill. Use whenever you need to run the evidence phases end-to-end: collecting raw evidence from the data lake, verifying live GitHub star counts, running Phase 2B benchmark-source verification, running adversarial auditing for noise and URL errors, and checking link health via Firecrawl. Also use when someone says "run the evidence pipeline", "verify the evidence lake", "audit evidence", "run ev-pipeline", "full evidence check", "pipeline run", "prepare evidence for ingestion", or "refresh the data lake". This is the pre-ingestion step — run it before importing any evidence into the registry.

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Write an ETL operational runbook. Use when the user says "ETL runbook", "pipeline runbook", "data job runbook", "how to operate this ETL", "on-call guide for data pipelines", "what to do when the data job fails", "pipeline troubleshooting guide", "data ops runbook", or needs to document how to operate, monitor, and recover a data pipeline or ETL job - even if they don't explicitly say "runbook".

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分阶段排查大量数据导入、历史补录、转换和同步的耗时，比较处理方法并加速，在执行后核对文件数、行数与时间戳的一致性，形成可重复使用的流程。

• 加速大量数据导入与导出
• 缩短历史数据补录时间
• 比较批次大小与并行任务数

Organize PyTorch models and data pipelines with PyTorch Lightning, automate training, save checkpoints, and track experiments. Includes templates and guidance for configuring training across multiple GPUs or TPUs.

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• Restructure PyTorch training code into LightningModules
• Build reusable data preparation and loading pipelines
• Configure checkpoints, early stopping, and experiment logs

Create, edit, and clean spreadsheet files, including Excel workbooks and CSV data. Add formulas, formatting, and charts, preserve existing template styles, and recalculate formulas to check for errors before delivering the spreadsheet.

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• Clean messy CSV rows and headers into a usable spreadsheet
• Add formulas and charts to an existing Excel workbook
• Build a financial model with editable assumptions

Set up a local web search server using Bright Data SERP API and unfancy-search. Retrieve results from multiple engines, optionally expand queries with AI, and receive combined rankings with duplicates removed and results grouped by domain.

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• Adding web search to a Claude Code agent
• Researching a topic through multiple related queries
• Filtering searches by region or domain

Connect cognee to additional databases and data sources through community packages. The skill covers installing and registering adapters, importing data through connectors, configuring monitoring, and developing extension packages with examples and tests.

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• Connect cognee to Qdrant or another database
• Import data from Slack or Gmail
• Configure monitoring with Keywords AI

Create and edit spreadsheet files, clean messy tables, and convert tabular formats. Add formulas, formatting, and charts while following existing template styles, then recalculate formulas and check for errors before delivering the spreadsheet.

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• Clean messy CSV data into a spreadsheet
• Add formulas and charts to an Excel workbook
• Update a workbook using its existing formatting

Build machine learning workflows that connect data checks, training, evaluation, and deployment. The skill implements experiment tracking, model version management, and automated retraining, with reproducible environments and explicit validation thresholds before models enter production.

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• Automate model training and retraining workflows
• Record and compare machine learning experiments
• Build a feature store for model input data

Develop and verify changes to Apache Beam's Python SDK. Set up a virtual environment, run tests, build installable packages or containers, and run data processing pipelines with the modified SDK.

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• Set up an Apache Beam Python SDK development environment
• Run unit and integration tests for SDK changes
• Build SDK packages and container images